Alzheimer disease is a neurological disorder that affects the elderly, caused by abnormal protein buildup in the brain. It leads to difficulties such as financial mismanagement, disorientation, behavioral changes, and repetitive speech. The existing methods use traditional features to detect the early signs of AD with low detection accuracy. The potential features have to be identified that represent best the patterns associated with alzheimers. Feature selection using ant lion optimization resolves the issue by using complementary information from hybrid features. The proposed HybridOpt pipeling for AD diagnosis combines the high level and low level features for early stage detection The objective of this work is to select efficient features from different deep networks, such as AlexNet, Googlenet, VGG16, ResNet, Efficient, DenseNet, and traditional texture features. Ant Lion Optimization is used to select the best feature among the deep network and traditional texture feature groups. Extensive experimentation on two highly challenging datasets called the Alzheimer’s disease neuroimage dataset and KAGGLE reveals that the proposed HybridOpt pipeline achieves an accuracy of 99% and 98.1% respectively.